上传文件至「Graduation Design」

2026-04-26 13:23:38 +08:00
parent 90808c671e
commit e9e142203e
5 changed files with 749 additions and 0 deletions
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+import torch
+import torch.nn as nn
+import torch.optim as optim
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+import time
+
+# ==========================================
+# 0. 全局设置
+# ==========================================
+plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
+plt.rcParams['axes.unicode_minus'] = False
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"========== 启动 AI 动态追踪生成器 ==========\n使用设备: {device}")
+
+N_ant, N_RF, d_lambda = 24, 4, 0.5
+noise_power = 1.0
+INR_linear = 10**(30/10) # 30dB 强杂波
+
+def gen_a_batch(theta_deg_tensor):
+    theta_rad = torch.deg2rad(theta_deg_tensor)
+    n_idx = torch.arange(-(N_ant-1)/2, (N_ant-1)/2 + 0.1, device=device).unsqueeze(0)
+    phases = 2 * torch.pi * d_lambda * n_idx * torch.sin(theta_rad.unsqueeze(1))
+    return torch.exp(1j * phases).unsqueeze(2)
+
+# ==========================================
+# 1. 广域追踪网络 (彻底解决相位死锁)
+# ==========================================
+class DynamicHBFNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(48, 512), nn.GELU(),
+            nn.Linear(512, 512), nn.GELU(),
+            nn.Linear(512, N_ant * N_RF * 2) 
+        )
+
+    def forward(self, w_opt):
+        w_flat = w_opt.squeeze(2)
+        x = torch.cat([w_flat.real, w_flat.imag], dim=1)
+        out = self.net(x).view(-1, N_ant, N_RF, 2)
+        complex_out = out[..., 0] + 1j * out[..., 1]
+        # 严格恒模约束
+        F_RF = complex_out / (torch.abs(complex_out) + 1e-8) / np.sqrt(N_ant)
+        return F_RF
+
+# ==========================================
+# 2. 广域动态特训
+# ==========================================
+model = DynamicHBFNet().to(device)
+optimizer = optim.Adam(model.parameters(), lr=0.002)
+
+Batch_Size, Epochs = 500, 10000      
+
+print("正在对 AI 进行大范围目标与杂波抗干扰特训...")
+for epoch in range(Epochs):
+    model.train()
+    optimizer.zero_grad()
+    
+    # 【动态大扫掠特训】：目标 0~40°，杂波 -20~-3°
+    theta_t = torch.rand(Batch_Size, device=device) * 40.0
+    theta_c = -20.0 + torch.rand(Batch_Size, device=device) * 17.0
+    
+    a_t, a_c = gen_a_batch(theta_t), gen_a_batch(theta_c)
+    
+    R_interf = noise_power * torch.eye(N_ant, device=device).unsqueeze(0) + INR_linear * (a_c @ a_c.mH)
+    w_opt = torch.linalg.pinv(R_interf) @ a_t
+    w_opt = w_opt / torch.norm(w_opt, dim=1, keepdim=True)
+    
+    F_RF = model(w_opt)
+    F_BB = torch.linalg.pinv(F_RF.mH @ F_RF + 1e-3*torch.eye(N_RF, device=device).unsqueeze(0)) @ (F_RF.mH @ w_opt)
+    w_ai = F_RF @ F_BB
+    w_ai = w_ai / torch.norm(w_ai, dim=1, keepdim=True)
+    
+    sim = torch.abs(torch.sum(w_opt.conj() * w_ai, dim=(1,2)))**2
+    loss_sim = torch.mean(1.0 - sim)
+    loss_null = torch.mean(torch.abs(torch.sum(a_c.conj() * w_ai, dim=(1,2)))**2)
+    
+    loss = loss_sim + 150.0 * loss_null 
+    loss.backward()
+    optimizer.step()
+
+print("特训完成！正在生成实时追踪 GIF 动图...")
+
+# ==========================================
+# 3. 生成精美动态 GIF
+# ==========================================
+model.eval()
+theta_scan = torch.arange(-60, 60.1, 0.5, device=device) # 限制扫描角度让图像更聚焦
+A_scan = gen_a_batch(theta_scan).squeeze(2).T 
+
+fig, ax = plt.subplots(figsize=(10, 6))
+ax.set_xlim(-60, 60)
+ax.set_ylim(-60, 0)
+ax.set_xlabel('角度 θ (°)', fontsize=12, fontweight='bold')
+ax.set_ylabel('归一化增益 (dB)', fontsize=12, fontweight='bold')
+ax.grid(True, ls='--')
+
+line_opt, = ax.plot([], [], 'k-', lw=2, alpha=0.5, label='纯数字最优下限')
+line_ai,  = ax.plot([], [], color='#D95319', ls='--', lw=2.5, label='AI 瞬时感知混合波束')
+vline_t   = ax.axvline(0, color='g', ls='-.', lw=2, label='机动目标轨迹')
+vline_c   = ax.axvline(0, color='r', ls='-.', lw=2, label='强杂波轨迹')
+
+title_text = ax.set_title('', fontsize=14, fontweight='bold')
+ax.legend(loc='lower center', fontsize=10)
+
+frames_count = 60 # 60帧平滑过渡
+
+def update(frame):
+    # 目标从 0 匀速扫到 40
+    t_ang = 0.0 + frame * (40.0 / (frames_count - 1))
+    # 杂波从 -3 匀速扫到 -20
+    c_ang = -3.0 - frame * (17.0 / (frames_count - 1))
+    
+    a_t = gen_a_batch(torch.tensor([t_ang], device=device))
+    a_c = gen_a_batch(torch.tensor([c_ang], device=device))
+    R_interf = noise_power * torch.eye(N_ant, device=device).unsqueeze(0) + INR_linear * (a_c @ a_c.mH)
+    
+    w_opt = torch.linalg.pinv(R_interf) @ a_t
+    w_opt /= torch.norm(w_opt, dim=1, keepdim=True)
+    
+    # AI 仅凭一次前向传播，瞬间生成波束！
+    with torch.no_grad():
+        F_RF = model(w_opt)
+        F_BB = torch.linalg.pinv(F_RF.mH @ F_RF + 1e-3*torch.eye(N_RF, device=device).unsqueeze(0)) @ (F_RF.mH @ w_opt)
+        w_ai = F_RF @ F_BB
+        w_ai /= torch.norm(w_ai, dim=1, keepdim=True)
+        
+    gain_opt = torch.abs(torch.sum(w_opt.conj() * A_scan.unsqueeze(0), dim=1))**2
+    bp_opt = 10 * torch.log10(gain_opt).cpu().numpy().flatten()
+    bp_opt -= np.max(bp_opt)
+    
+    gain_ai = torch.abs(torch.sum(w_ai.conj() * A_scan.unsqueeze(0), dim=1))**2
+    bp_ai = 10 * torch.log10(gain_ai).cpu().numpy().flatten()
+    bp_ai -= np.max(bp_ai)
+    
+    line_opt.set_data(theta_scan.cpu().numpy(), bp_opt)
+    line_ai.set_data(theta_scan.cpu().numpy(), bp_ai)
+    vline_t.set_xdata([t_ang, t_ang])
+    vline_c.set_xdata([c_ang, c_ang])
+    
+    title_text.set_text(f'AI 端到端波束实时追踪: 目标 {t_ang:.1f}° | 强杂波 {c_ang:.1f}°')
+    return line_opt, line_ai, vline_t, vline_c, title_text
+
+# 渲染动图
+anim = animation.FuncAnimation(fig, update, frames=frames_count, interval=100, blit=False)
+anim.save('E:\\Coding\\graduation design\\MIMO_Beamforming_Simulation_Platform\\final\\python\\Deeplearning\\AI_Beam_Tracking.gif', writer='pillow', fps=10)